Infographic
Published June 5, 2025
Fintech Banks Credit Unions Advanced Analytics & Modeling Credit Decisioning Marketing
Based on interviews with global financial institutions, the Forrester Total Economic Impact™ study, commissioned by Experian, reveals real customer impact stories and business wins delivered by the Experian Ascend Platform™. This infographic shares some of the key findings.
Key insights include:
- 183% return on investment (ROI) and $13.3M in net present value (NPV) over three years
- A car leasing company’s approval rates increased from 60% to 66%, improving consistency and revenue growth
- An airline reduced fraud chargebacks by 99.9%, lowering regulatory risks and financial losses.
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The imposter in the session: The hub and the hive
Uncover a coordinated account takeover fraud ring in action and learn how it was stopped using behavioral, device and network intelligence.
Key insights:
- Fraud rings operate with structured roles to efficiently execute account takeover attacks
- Compromised accounts can be connected across devices, revealing coordinated activity
- Behavioral, device and network intelligence expose fraud signals traditional tools miss
- Stopping ATO requires identifying and dismantling the full fraud ring, not just individual logins
Case Study
How one lender achieved a 5 month payback
Education Credit Union modernized underwriting with Experian’s Advanced Decisioning—driving faster decisions, stronger approvals, and rapid ROI in a competitive market.
- 5-month payback period
- 4.4% lift in performance & funding
- $4M+ incremental originations
Video
5 model classification blind spots to watch out for in 2026
Model classification now drives governance in AI environments—but many frameworks lag reality. Static, inconsistent approaches create risk as inventories grow.
- Refresh classification continuously
- Map dependencies and shadow models
- Standardize decisions and documentation
White Paper
Why efficient hyperparameter tuning matters: Putting Bayesian Optimization to the test
Bayesian Optimization offers a more efficient way to tune machine learning models at scale. This white paper presents a controlled comparison with Grid Search using production credit-risk data to measure runtime and model performance.
- Up to 80× faster hyperparameter tuning
- No loss in CV or holdout AUC
- Evidence from LightGBM credit-risk models
- Guidance for governed ML environments